In this paper, we show that the domain shift induced by the difference in reconstruction kernels decreases the quality of the COVID-19 segmentation algorithms. To do so, we construct two domains from the publicly available data: the source domain with the smooth reconstruction kernels and target domain with the sharp reconstruction kernels. See the detailed data description in Sec. . We train the segmentation model on the source domain and test it on the target domain. With the observed decrease of test score, we then validate the most relevant domain adaptation methods. In our comparison, we include an augmentation approach [1]}, unsupervised adversarial learning [2]}, and our proposed feature maps consistency regularization. We describe all methods in Sec. .
